AI evidence extraction

ADOPTION AND USE OF CHATGPT AMONG STUDENTS IN NORWEGIAN HIGHER EDUCATION: A UTAUT2-BASED STUDY

Piyatara Charika Kanahala, David Anis Habibi, Lester Allan Lasrado, Moutaz Haddara · Journal of the Association for Information Systems · 2025

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6/10

Relevance

0/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

Methodology & findings

Study design

Cross-sectional survey study using the UTAUT2 (Unified Theory of Acceptance and Use of Technology 2) framework with 219 student respondents from two Norwegian higher education institutions.

Sample

N = 219, 2 groups

Primary method

Methods not explicitly detailed in abstract. Likely includes structural equation modeling (SEM) or path analysis given the UTAUT2 framework application, but specific statistical software and procedures are not stated in the available abstract.

Main result

The study found that "key factors of behavioral intention include habit, effort expectancy, and social influence, while concerns about over-reliance, particularly on critical thinking, act as main barriers." Additionally, "an interaction effect revealed that performance expectancy has a stronger influence on behavioural intention among novice users than experienced ones."

Reports effect sizes.

Research paradigm

Positivist/Quantitative

Author conclusions

The authors conclude that their study "attempts to contribute to emerging literature on GenAI adoption and use in education and to provide practical insights for aligning institutional policies with diverse student needs to foster more effective AI integration in higher education." They also "signify the potential importance of customized academic-level support."

Risk of bias

Selection bias: Participants self-selected from two Norwegian institutions (not representative of all Norwegian or international students); Self-report bias: Survey-based methodology relies on self-reported adoption and use behaviors; Institutional context bias: Results specific to Norwegian higher education context; Temporal bias: Study conducted at a specific point in time during rapid AI adoption; Selection bias: Students from only two Norwegian institutions may not represent broader higher education populations; Self-selection bias: Participation may be voluntary, attracting students with particular interest in or attitudes toward AI; Cross-sectional design: Cannot establish causal relationships, only associations; Response bias: Self-reported behavioral intention and adoption measures; Self-selection bias: Student participation was voluntary, potentially attracting those more interested in or favorable toward ChatGPT; Cross-sectional design: Cannot establish causal relationships or temporal ordering; Limited geographic scope: Data from only two Norwegian institutions may limit generalizability; Sample size considerations: 219 students across two institutions may be insufficient for reliable interaction effect detection; Potential social desirability bias in self-reported adoption and use

Open questions raised

The study identifies a gap in "empirical evidence on student adoption" of generative AI and suggests the need for "customized academic-level support" and alignment of "institutional policies with diverse student needs" for effective AI integration in higher education.
The authors identify the need for customized academic-level support and note potential challenges in institutional strategies across academic levels. They emphasize the importance of aligning institutional policies with diverse student needs for more effective AI integration in higher education.
The authors identify that empirical evidence on student adoption of generative AI remains limited. They note the importance of understanding institutional strategies across different academic levels and highlight the need for customized support approaches tailored to diverse student needs in AI adoption.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 63%

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